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This paper is in the following e-collection/theme issue:

Machine Learning (3350) Clinical Informatics (2399) Imaging Informatics (511) Orthopedics (154) Medical imaging (205)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/86155, first published .
AI robot analyzing knee joint scans in a futuristic medical setting

Enhanced Magnetic Resonance Imaging–Based Knee Cartilage Segmentation Using a Swin-UNet Conditional Generative Adversarial Network: Development and Validation Study

Enhanced Magnetic Resonance Imaging–Based Knee Cartilage Segmentation Using a Swin-UNet Conditional Generative Adversarial Network: Development and Validation Study

Authors of this article:

Jun Young Park1 Author Orcid Image ;   Ji-Hoon Nam2, 3, 4 Author Orcid Image ;   Shakhboz Abdigapporov3 Author Orcid Image ;   Jong-Keun Kim5 Author Orcid Image ;   Yong-Gon Koh6 Author Orcid Image ;   Byung Woo Cho7 Author Orcid Image ;   Hyuck Min Kwon8 Author Orcid Image ;   Kwan Kyu Park8 Author Orcid Image ;   Kyoung-Tak Kang2, 3, 4 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Li L, Xing Y, Xiong W, Liao S, Zou B, Sun X, Zhi L. SwMrNet: A Multi-Target Tissue Segmentation Method for Robust and Accurate Clinical Knee Diagnosis Assistance. Bioengineering 2026;13(7):784 View
  2. Zhang B, Yu X, Zhang Z, Zhou X, Zhao W, Zhao C. Artificial intelligence in the biomechanics of knee osteoarthritis: biomechanical phenotyping, risk stratification, diagnosis, treatment and clinical translation—a narrative review. Frontiers in Bioengineering and Biotechnology 2026;14 View